--- title: "future-agi vs PentestGPT" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-future-agi-vs-greydgl-pentestgpt" tools: ["future-agi-future-agi", "greydgl-pentestgpt"] --- # future-agi vs PentestGPT *GraphCanon updated Sep 20, 2026* ## Verdict Pick future-agi if future-AGI is an open-source platform for evaluating and observing LLM and AI agent applications, offering features like tracing, evaluations, simulations, and guardrails. It is self-hostable and supports deployment via,; pick PentestGPT if pentestGPT is a Python-based automated penetration testing framework that uses large language models for security testing. [future-agi](https://futureagi.com) reports 2.0k GitHub stars, 627 forks, and 961 open issues, last pushed Sep 18, 2026. [PentestGPT](https://github.com/GreyDGL/PentestGPT) has 16k stars, 2.7k forks, and 81 open issues, last pushed Jul 14, 2026. Figures are from public GitHub metadata via [future-agi's repository](https://github.com/future-agi/future-agi) and [PentestGPT's repository](https://github.com/GreyDGL/PentestGPT). | | [future-agi](/tools/future-agi-future-agi.md) | [PentestGPT](/tools/greydgl-pentestgpt.md) | | --- | --- | --- | | Tagline | Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications | Automated Penetration Testing Agentic Framework Powered by Large Language Models | | Stars | 2,032 | 15,514 | | Forks | 627 | 2,699 | | Open issues | 961 | 81 | | Language | Python | Python | | Adopt for | Future-AGI is an open-source platform for evaluating and observing LLM and AI agent applications, offering features like tracing, evaluations, simulations, and guardrails. It is self-hostable and supports deployment via, | PentestGPT is a Python-based automated penetration testing framework that uses large language models for security testing. | | Persona | - | - | | Runtime | - | - | | License | Apache License 2.0, allowing for free use, modification, and distribution of the software, with the condition that any derivative works also be licensed under the same terms. | MIT License, for educational and authorized security testing only, use at your own risk. | | Categories | AI Agents, Evaluation & Observability, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [future-agi](/tools/future-agi-future-agi.md) | [PentestGPT](/tools/greydgl-pentestgpt.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 65d | | Open issues (now) | 961 | 81 | | Stars delta | +473 (30d) | +614 (30d) | | Open issues delta | +365 (30d) | +16 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/future-agi-future-agi/trust.md) | [trust report](/tools/greydgl-pentestgpt/trust.md) | ## Decision facts: future-agi - **Pricing:** freemium - The core platform is free and open-source under the Apache License 2.0. However, for managed services or additional support, contact sales for pricing. - **Requirements:** Min 4 GB RAM; Requires Docker; Future-AGI requires Docker for deployment, with support for Docker Compose and upcoming Kubernetes and Helm support.; For production environments, a setup script is provided to generate secrets and pin image tags, ensuring a secure and reproducible deployment. - **Adopt for:** Future-AGI is an open-source platform for evaluating and observing LLM and AI agent applications, offering features like tracing, evaluations, simulations, and guardrails. It is self-hostable and supports deployment via, - **License detail:** Apache License 2.0, allowing for free use, modification, and distribution of the software, with the condition that any derivative works also be licensed under the same terms. ## Decision facts: PentestGPT - **Adopt for:** PentestGPT is a Python-based automated penetration testing framework that uses large language models for security testing. - **License detail:** MIT License, for educational and authorized security testing only, use at your own risk. ## Choose when ### Choose future-agi if… - License: future-agi is Apache-2.0, PentestGPT is MIT. - Pricing: The core platform is free and open-source under the Apache License 2.0. However, for managed services or additional support, contact sales for pricing.. - Requirements: Min 4 GB RAM; Requires Docker; Future-AGI requires Docker for deployment, with support for Docker Compose and upcoming Kubernetes and Helm support.; For production environments, a setup script is provided to generate secrets and pin image tags, ensuring a secure and reproducible deployment.. - Tags unique to future-agi: ai-agents, ai-evals, ai-gateway, ai-optimization. - Also covers Evaluation & Observability. - You need a self-hostable solution that does not phone home, ensuring full control over your data and evaluation logic. ### Choose PentestGPT if… - License: PentestGPT is MIT, future-agi is Apache-2.0. - Tags unique to PentestGPT: automated-testing, docker, large-language-models, large-language-models-integration. - When you need an automated penetration testing tool that leverages large language models for enhanced security analysis. ## When NOT to use future-agi - You require immediate Kubernetes or Helm support, as these are not yet available, though they are in development. - You are seeking a managed service or a solution available through AWS Marketplace, as these options are not yet available, though they are planned for the future. - Your project is in a highly dynamic environment where frequent updates and vendor support are critical, as Future-AGI is a community-driven project with a focus on self-hosting and open-source. ## When NOT to use PentestGPT - If your project requires a tool that does not use large language models for penetration testing. - When you need a tool that does not support Docker or does not offer persistent login sessions. - For unauthorized security testing or any illegal activities, as the tool is strictly for educational and authorized testing. ## Common questions ### What is the difference between future-agi and PentestGPT? future-agi: Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. PentestGPT: Automated Penetration Testing Agentic Framework Powered by Large Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose future-agi over PentestGPT? Choose future-agi over PentestGPT when License: future-agi is Apache-2.0, PentestGPT is MIT; Pricing: The core platform is free and open-source under the Apache License 2.0. However, for managed services or additional support, contact sales for pricing.; Requirements: Min 4 GB RAM; Requires Docker; Future-AGI requires Docker for deployment, with support for Docker Compose and upcoming Kubernetes and Helm support.; For production environments, a setup script is provided to generate secrets and pin image tags, ensuring a secure and reproducible deployment.; Tags unique to future-agi: ai-agents, ai-evals, ai-gateway, ai-optimization; Also covers Evaluation & Observability; You need a self-hostable solution that does not phone home, ensuring full control over your data and evaluation logic. ### When should I choose PentestGPT over future-agi? Choose PentestGPT over future-agi when License: PentestGPT is MIT, future-agi is Apache-2.0; Tags unique to PentestGPT: automated-testing, docker, large-language-models, large-language-models-integration; When you need an automated penetration testing tool that leverages large language models for enhanced security analysis. ### When should I avoid future-agi? You require immediate Kubernetes or Helm support, as these are not yet available, though they are in development. You are seeking a managed service or a solution available through AWS Marketplace, as these options are not yet available, though they are planned for the future. Your project is in a highly dynamic environment where frequent updates and vendor support are critical, as Future-AGI is a community-driven project with a focus on self-hosting and open-source. ### When should I avoid PentestGPT? If your project requires a tool that does not use large language models for penetration testing. When you need a tool that does not support Docker or does not offer persistent login sessions. For unauthorized security testing or any illegal activities, as the tool is strictly for educational and authorized testing. ### Is future-agi or PentestGPT more popular on GitHub? PentestGPT has more GitHub stars (15,514 vs 2,032). Stars measure visibility, not whether either tool fits your constraints. ### Are future-agi and PentestGPT open source? Yes - both are open-source projects on GitHub (future-agi: Apache-2.0, PentestGPT: MIT). ### Where can I find alternatives to future-agi or PentestGPT? GraphCanon lists graph-backed alternatives at [future-agi alternatives](/tools/future-agi-future-agi/alternatives) and [PentestGPT alternatives](/tools/greydgl-pentestgpt/alternatives) ([future-agi markdown twin](/tools/future-agi-future-agi/alternatives.md), [PentestGPT markdown twin](/tools/greydgl-pentestgpt/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/future-agi-future-agi-vs-greydgl-pentestgpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, future-agi or PentestGPT? future-agi: Very active. PentestGPT: Steady. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for future-agi and PentestGPT? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [future-agi trust report](/tools/future-agi-future-agi/trust); [PentestGPT trust report](/tools/greydgl-pentestgpt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=future-agi-future-agi`](/api/graphcanon/graph?tool=future-agi-future-agi) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_